



'''simple docstring'''


import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[Any]    ):
						lowercase   =				VideoMAEConfig()

						set_architecture_configs(a__			,	a__    )

						if "finetuned" not in model_name:
												lowercase   =				False

						if "finetuned" in model_name:
												lowercase   =				'huggingface/label-files'
												if "kinetics" in model_name:
																		lowercase   =				400
																		lowercase   =				'kinetics400-id2label.json'
												elif "ssv2" in model_name:
																		lowercase   =				174
																		lowercase   =				'something-something-v2-id2label.json'
												else:
																		raise ValueError("""Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned."""    )
												lowercase   =				json.load(open(hf_hub_download(a__			,	a__			,	repo_type="""dataset"""    )			,	"""r"""    )    )
												lowercase   =				{int(a__    ): v for k, v in idalabel.items()}
												lowercase   =				idalabel
												lowercase   =				{v: k for k, v in idalabel.items()}

						return config







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Tuple			,	lowercase_     :   List[str]    ):






						if "small" in model_name:
												lowercase   =				384
												lowercase   =				1536
												lowercase   =				12
												lowercase   =				16
												lowercase   =				12
												lowercase   =				3
												lowercase   =				192
												lowercase   =				768
						elif "large" in model_name:
												lowercase   =				1024
												lowercase   =				4096
												lowercase   =				24
												lowercase   =				16
												lowercase   =				12
												lowercase   =				8
												lowercase   =				512
												lowercase   =				2048
						elif "huge" in model_name:
												lowercase   =				1280
												lowercase   =				5120
												lowercase   =				32
												lowercase   =				16
												lowercase   =				12
												lowercase   =				8
												lowercase   =				640
												lowercase   =				2560
						elif "base" not in model_name:
												raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""    )







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Any    ):






						if "encoder." in name:
												lowercase   =				name.replace("""encoder."""			,	""""""    )
						if "cls_token" in name:
												lowercase   =				name.replace("""cls_token"""			,	"""videomae.embeddings.cls_token"""    )
						if "decoder_pos_embed" in name:
												lowercase   =				name.replace("""decoder_pos_embed"""			,	"""decoder.decoder_pos_embed"""    )
						if "pos_embed" in name and "decoder" not in name:
												lowercase   =				name.replace("""pos_embed"""			,	"""videomae.embeddings.position_embeddings"""    )
						if "patch_embed.proj" in name:
												lowercase   =				name.replace("""patch_embed.proj"""			,	"""videomae.embeddings.patch_embeddings.projection"""    )
						if "patch_embed.norm" in name:
												lowercase   =				name.replace("""patch_embed.norm"""			,	"""videomae.embeddings.norm"""    )
						if "decoder.blocks" in name:
												lowercase   =				name.replace("""decoder.blocks"""			,	"""decoder.decoder_layers"""    )
						if "blocks" in name:
												lowercase   =				name.replace("""blocks"""			,	"""videomae.encoder.layer"""    )
						if "attn.proj" in name:
												lowercase   =				name.replace("""attn.proj"""			,	"""attention.output.dense"""    )
						if "attn" in name and "bias" not in name:
												lowercase   =				name.replace("""attn"""			,	"""attention.self"""    )
						if "attn" in name:
												lowercase   =				name.replace("""attn"""			,	"""attention.attention"""    )
						if "norm1" in name:
												lowercase   =				name.replace("""norm1"""			,	"""layernorm_before"""    )
						if "norm2" in name:
												lowercase   =				name.replace("""norm2"""			,	"""layernorm_after"""    )
						if "mlp.fc1" in name:
												lowercase   =				name.replace("""mlp.fc1"""			,	"""intermediate.dense"""    )
						if "mlp.fc2" in name:
												lowercase   =				name.replace("""mlp.fc2"""			,	"""output.dense"""    )
						if "decoder_embed" in name:
												lowercase   =				name.replace("""decoder_embed"""			,	"""decoder.decoder_embed"""    )
						if "decoder_norm" in name:
												lowercase   =				name.replace("""decoder_norm"""			,	"""decoder.decoder_norm"""    )
						if "decoder_pred" in name:
												lowercase   =				name.replace("""decoder_pred"""			,	"""decoder.decoder_pred"""    )
						if "norm.weight" in name and "decoder" not in name and "fc" not in name:
												lowercase   =				name.replace("""norm.weight"""			,	"""videomae.layernorm.weight"""    )
						if "norm.bias" in name and "decoder" not in name and "fc" not in name:
												lowercase   =				name.replace("""norm.bias"""			,	"""videomae.layernorm.bias"""    )
						if "head" in name and "decoder" not in name:
												lowercase   =				name.replace("""head"""			,	"""classifier"""    )

						return name







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   List[str]			,	lowercase_     :   str    ):






						for key in orig_state_dict.copy().keys():
												lowercase   =				orig_state_dict.pop(a__    )

												if key.startswith("""encoder."""    ):
																		lowercase   =				key.replace("""encoder."""			,	""""""    )

												if "qkv" in key:
																		lowercase   =				key.split("""."""    )
																		if key.startswith("""decoder.blocks"""    ):
																								lowercase   =				config.decoder_hidden_size
																								lowercase   =				int(key_split[2]    )
																								lowercase   =				'decoder.decoder_layers.'
																								if "weight" in key:
																														lowercase   =				val[:dim, :]
																														lowercase   =				val[dim : dim * 2, :]
																														lowercase   =				val[-dim:, :]
																		else:
																								lowercase   =				config.hidden_size
																								lowercase   =				int(key_split[1]    )
																								lowercase   =				'videomae.encoder.layer.'
																								if "weight" in key:
																														lowercase   =				val[:dim, :]
																														lowercase   =				val[dim : dim * 2, :]
																														lowercase   =				val[-dim:, :]
												else:
																		lowercase   =				val

						return orig_state_dict







def   SCREAMING_SNAKE_CASE					(					):
						lowercase   =				hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""			,	filename="""eating_spaghetti.npy"""			,	repo_type="""dataset"""    )
						lowercase   =				np.load(a__    )
						return list(a__    )







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   List[str]			,	lowercase_     :   Union[str, Any]			,	lowercase_     :   Union[str, Any]			,	lowercase_     :   int    ):
						lowercase   =				get_videomae_config(a__    )

						if "finetuned" in model_name:
												lowercase   =				VideoMAEForVideoClassification(a__    )
						else:
												lowercase   =				VideoMAEForPreTraining(a__    )

						# download original checkpoint, hosted on Google Drive
						lowercase   =				'pytorch_model.bin'
						gdown.cached_download(a__			,	a__			,	quiet=a__    )
						lowercase   =				torch.load(a__			,	map_location="""cpu"""    )
						if "model" in files:
												lowercase   =				files['model']
						else:
												lowercase   =				files['module']
						lowercase   =				convert_state_dict(a__			,	a__    )

						model.load_state_dict(a__    )
						model.eval()

						# verify model on basic input
						lowercase   =				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,	image_std=[0.5, 0.5, 0.5]    )
						lowercase   =				prepare_video()
						lowercase   =				image_processor(a__			,	return_tensors="""pt"""    )

						if "finetuned" not in model_name:
												lowercase   =				hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""			,	filename="""bool_masked_pos.pt"""    )
												lowercase   =				torch.load(a__    )

						lowercase   =				model(**a__    )
						lowercase   =				outputs.logits

						lowercase   =				[
						    'videomae-small-finetuned-kinetics',
						    'videomae-small-finetuned-ssv2',
						    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
						    'videomae-base-short',
						    'videomae-base-short-finetuned-kinetics',
						    'videomae-base',
						    'videomae-base-finetuned-kinetics',
						    'videomae-large',
						    'videomae-large-finetuned-kinetics',
						    'videomae-huge-finetuned-kinetics',
						    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
						    'videomae-base-short-ssv2',
						    'videomae-base-short-finetuned-ssv2',
						    'videomae-base-ssv2',
						    'videomae-base-finetuned-ssv2',
						]

						# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
						if model_name == "videomae-small-finetuned-kinetics":
												lowercase   =				torch.Size([1, 400]    )
												lowercase   =				torch.tensor([-0.9_291, -0.4_061, -0.9_307]    )
						elif model_name == "videomae-small-finetuned-ssv2":
												lowercase   =				torch.Size([1, 174]    )
												lowercase   =				torch.tensor([0.2_671, -0.4_689, -0.8_235]    )
						elif model_name == "videomae-base":
												lowercase   =				torch.Size([1, 1408, 1536]    )
												lowercase   =				torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]    )
						elif model_name == "videomae-base-short":
												lowercase   =				torch.Size([1, 1408, 1536]    )
												lowercase   =				torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]    )
												# we verified the loss both for normalized and unnormalized targets for this one
												lowercase   =				torch.tensor([0.5_142]    ) if config.norm_pix_loss else torch.tensor([0.6_469]    )
						elif model_name == "videomae-large":
												lowercase   =				torch.Size([1, 1408, 1536]    )
												lowercase   =				torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]    )
						elif model_name == "videomae-large-finetuned-kinetics":
												lowercase   =				torch.Size([1, 400]    )
												lowercase   =				torch.tensor([0.0_771, 0.0_011, -0.3_625]    )
						elif model_name == "videomae-huge-finetuned-kinetics":
												lowercase   =				torch.Size([1, 400]    )
												lowercase   =				torch.tensor([0.2_433, 0.1_632, -0.4_894]    )
						elif model_name == "videomae-base-short-finetuned-kinetics":
												lowercase   =				torch.Size([1, 400]    )
												lowercase   =				torch.tensor([0.6_588, 0.0_990, -0.2_493]    )
						elif model_name == "videomae-base-finetuned-kinetics":
												lowercase   =				torch.Size([1, 400]    )
												lowercase   =				torch.tensor([0.3_669, -0.0_688, -0.2_421]    )
						elif model_name == "videomae-base-short-ssv2":
												lowercase   =				torch.Size([1, 1408, 1536]    )
												lowercase   =				torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]    )
						elif model_name == "videomae-base-short-finetuned-ssv2":
												lowercase   =				torch.Size([1, 174]    )
												lowercase   =				torch.tensor([-0.0_537, -0.1_539, -0.3_266]    )
						elif model_name == "videomae-base-ssv2":
												lowercase   =				torch.Size([1, 1408, 1536]    )
												lowercase   =				torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]    )
						elif model_name == "videomae-base-finetuned-ssv2":
												lowercase   =				torch.Size([1, 174]    )
												lowercase   =				torch.tensor([0.1_961, -0.8_337, -0.6_389]    )
						else:
												raise ValueError(F"""Model name not supported. Should be one of {model_names}"""    )

						# verify logits
						assert logits.shape == expected_shape
						if "finetuned" in model_name:
												assert torch.allclose(logits[0, :3]			,	a__			,	atol=1E-4    )
						else:
												print("""Logits:"""			,	logits[0, :3, :3]    )
												assert torch.allclose(logits[0, :3, :3]			,	a__			,	atol=1E-4    )
						print("""Logits ok!"""    )

						# verify loss, if applicable
						if model_name == "videomae-base-short":
												lowercase   =				outputs.loss
												assert torch.allclose(a__			,	a__			,	atol=1E-4    )
												print("""Loss ok!"""    )

						if pytorch_dump_folder_path is not None:
												print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""    )
												image_processor.save_pretrained(a__    )
												model.save_pretrained(a__    )

						if push_to_hub:
												print("""Pushing to the hub..."""    )
												model.push_to_hub(a__			,	organization="""nielsr"""    )


if __name__ == "__main__":
						lowercase_   :					Optional[int]        =     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
						    type=str,
						    help=(
						        '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
						        ''' download link.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
						    type=str,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						lowercase_   :					Optional[int]        =     parser.parse_args()
						convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)










"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			len(a__ )
						print('The following activities are selected:' )

						# The first activity is always selected
						lowerCAmelCase   :Dict						=			0
						print(a__     ,      end=',' )

						# Consider rest of the activities
						for j in range(a__ ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(a__     ,      end=',' )
																		lowerCAmelCase   :List[str]						=			j


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__SCREAMING_SNAKE_CASE   =   [1, 3, 0, 5, 8, 5]
		__SCREAMING_SNAKE_CASE   =   [2, 4, 6, 7, 9, 9]
		print_max_activities(start, finish)